Gray Codes for Partial Match and Range Queries
IEEE Transactions on Software Engineering
Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Tell me where i am so i can meet you sooner: asynchronous rendezvous with location information
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Almost optimal asynchronous rendezvous in infinite multidimensional grids
DISC'10 Proceedings of the 24th international conference on Distributed computing
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We present two new density-based algorithms for clustering data points in lower dimensions (dimensions ≤ 10). Both our algorithms compute density-based clusters and noises in O(n) CPU time, space, and I/O cost, under some reasonable assumptions, where n is the number of input points. Besides packing the data structure into buckets and using block access techniques to reduce the I/O cost, our algorithms apply space-filling curve techniques to reduce the disk access operations. Our first algorithm (Algorithm A) focuses on handling not highly clustered input data, while the second algorithm (Algorithm B) focuses on highly clustered input data. We implemented our algorithms, evaluated the effects of various space-filling curves, identified the best space-filling curve for our approaches, and conducted extensive performance evaluation. The experiments show the high performance of our algorithms. We believe that our algorithms are of considerable practical value.